These teachings relate generally to pulse oximeters and, more specifically, to pulse oximeters with algorithms embedded for real-time detection of motion and noise artifacts.
A pulse oximeter (PO) is a non-invasive, low cost device that is widely used in hospitals and clinics to monitor heart rate (HR) and arterial oxygen saturation (SpO2). Recently, there have been efforts to derive other physiological parameters from Photoplethysmogram (PPG), as recorded by a PO. The fluctuations observed in a PPG are influenced by arterial, and venous blood, as well as autonomic and respiratory systems of the peripheral circulation. Such information could be used to more comprehensively phenotype cardiovascular health. Due to increasing health care costs, a single sensor from which multiple clinical datapoints can be derived such as a PO is very attractive from a financial perspective. Moreover, utilizing a PO as a multipurpose vital sign monitor has a clinical appeal, since the device is widely accepted by clinicians and patients because of its ease of use, comfort and accuracy in providing reliable vital signs. Knowledge of respiratory rate and HR patterns would provide useful clinical information in many situations where a PO is the sole available monitor. However, extraction of the above mentioned vital signs and other physiological parameters using PO is predicated on artifact-free PPG data. It is well known that the PPG is highly sensitive to artifacts, particularly those generated while the patient is in motion. This imposes a huge limitation on the usability of the PPG for ambulatory monitoring applications. Motion and noise artifacts (MNA) distorting PPG recordings can cause erroneous estimation of HR and SpO2. Although the intelligent design of sensor attachment, form factors and packaging can help to reduce the impact of motion disturbances by making sure that the sensor is securely mounted, they are not sufficient for complete MNA removal. Combating MNA in PPG has been the core focus of research for many years.
Although there are techniques which have been proposed to alleviate the effects of MNA, solutions to this problem still remain unsatisfactory in practice. Several algorithm-based MNA reduction methods have been proposed, such as time and frequency domain filtering, power spectrum analysis, and blind source separation techniques. These techniques reconstruct noise contaminated PPG such that a noise-reduced signal is obtained. However, the reconstructed signal typically contains incomplete dynamic features of the uncorrupted PPG signal and some algorithms are solely designed to capture only the HR and SpO2 information instead of the signal's morphology and its amplitudes, which are needed for other physiological derivations. Moreover, these reconstruction algorithms operate even on clean PPG portions where MNA reduction is not needed. This introduces unnecessary computation burden and distorts the signal integrity of the clean portion of the data. Hence, an accurate MNA detection algorithm, which identifies clean PPG recordings from corrupted portions, is essential for the subsequent MNA reduction algorithm so that it does not distort the non-corrupted data segments.
MNA detection methods are mostly based on a signal quality index (SQI) which quantifies the severity of the artifacts. Some approaches quantify the SQI using waveform morphologies or filtered output, while other derive the SQI with the help of additional hardware such as accelerometer and electrocardiogram. Statistical measures, such as skewness, kurtosis, Shannon entropy, and Renyi's entropy, have been shown to be helpful in determining the SQI. These statistical algorithms differentiate the distribution of amplitudes between PPG segments with an assumption that clean and corrupt segments would form two separate groups. However, PPG waveforms vary among patients, thus yielding multitude of amplitude distributions. Therefore, it would be difficult to obtain high accuracy from these algorithms in practice. A recently published MNA detection method uses time-domain features such as variability in heart rate, amplitude, and waveform morphology with the help of the support vector machine (SVM) classifier for detection. The algorithm, which was termed time-domain variability SVM (TDV-SVM) is shown to be more robust than other statistical-based algorithms as it uses successive difference and variability measures. However, this method is highly dependent on accuracy of the peak amplitude detection. Unlike the electrocardiogram (ECG), the PPG waveform does not have distinctive peaks which make accurate peak detection challenging. The dependency on a peak detection subroutine is a drawback of the TDV-SVM algorithm and inevitably affects its performance. Time-frequency (TF) techniques such as Smoothed Pseudo Wigner-Ville, Short Time Fourier Transform, Continuous Wavelet Transform, Hilbert-Huang Transform, and Variable Frequency Complex Demodulation (VFCDM) received considerable attention as means to analysis the signal of interest in both temporal and spectral domains.
While PPG signals are obtained from a pulse oximeter, other devices can produce signals that behave like PPG signals. For example, color video images obtained from handheld mobile devices (such as smartphones) behave as reflection PPG images
There is therefore a need to provide a pulse oximeter with real-time detection of MNA to mitigate false readings of heart rates and oxygen saturation values during body movements, which can lead to a wider use of pulse oximeter device for ambulatory applications.
There is therefore also a need there is, therefore, to provide a device that uses signals that behave as PPG signals, where the device has a component for real-time detection of MNA to mitigate false readings of heart rates and oxygen saturation values during body movements, which can lead to a wider use of the device for ambulatory applications.